electricsheepafrica/africa-ilo-une-deap-sex-geo-mts-rt-unemployment-rate-by-sex-rural-urban-area-and-mari

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 10K<n<100K tags: - tabular - africa - ilostat - unemployment - ilo - labour - employment pretty_name: "Unemployment rate by sex, rural / urban area and marital status (%) | Africa (ILOSTAT)" --- # Unemployment rate by sex, rural / urban area and marital status (%) | Africa (ILOSTAT) 🌍 **16,738 observations** · **45 Africa countries** · **1994–2025** · *Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*      ## TL;DR This dataset contains **16,738 observations** of `Unemployment` data across **45 Africa countries**, spanning **1994–2025**, covering **1 distinct indicators**. ## About the source **ILOSTAT** is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets — drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation. - **Source:** [ILOSTAT](https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_GEO_MTS_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Unemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=UNE_DEAP_SEX_GEO_MTS_RT` and filtered to Africa ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the `source.label` column for traceability. ## Geographic coverage 45 Africa countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `EGY` | 1,455 | 2008 | 2024 | | `ZAF` | 1,404 | 2008 | 2024 | | `TUN` | 1,102 | 2006 | 2023 | | `GHA` | 808 | 2000 | 2024 | | `RWA` | 775 | 2014 | 2025 | | `AGO` | 766 | 2004 | 2025 | | `MLI` | 740 | 2013 | 2024 | | `ZMB` | 620 | 2015 | 2024 | | `ZWE` | 535 | 2011 | 2024 | | `NAM` | 514 | 1994 | 2018 | | `SEN` | 500 | 2011 | 2024 | | `NGA` | 449 | 2011 | 2024 | | `KEN` | 444 | 1999 | 2022 | | `TZA` | 430 | 2001 | 2020 | | `BFA` | 400 | 2014 | 2024 | | ... | _30 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_GEO_MTS_RT` — Unemployment rate by sex, rural / urban area and marital status (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AGO` | | `ref_area.label` | `string` | Country name in English | `Angola` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:13951` | | `source.label` | `string` | Source name in English | `LFS - Employment Survey` | | `indicator` | `string` | ILOSTAT indicator code | `UNE_DEAP_SEX_GEO_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex, rural / urb…` | | `sex` | `string` | Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female) | `SEX_T` | | `sex.label` | `string` | — | `Total` | | `classif1` | `string` | First classification variable (age, education, status, etc.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `10.391` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_indicator` | `string` | — | `I11:264` | | `note_indicator.label` | `string` | — | `Break in series: Methodology revised` | | `note_source` | `string` | — | `R1:3513` | | `note_source.label` | `string` | — | `Repository: ILO-STATISTICS - Micro da…` | ## Disaggregation dimensions The following columns provide disaggregation dimensions: - **`sex`** (3 unique values): `SEX_T`, `SEX_M`, `SEX_F` ## Data quality & caveats - Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here. - When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used. - Disaggregation columns (`sex`, `classif1`, `classif2`) are non-null only when the indicator publishes that breakdown. ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-ilo-une-deap-sex-geo-mts-rt-unemployment-rate-by-sex-rural-urban-area-and-mari") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python kenya = df[df["ref_area"] == "KEN"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "UNE_DEAP_SEX_GEO_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_GEO_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_GEO_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{africa_ilo_une_deap_sex_geo_mts_rt_unemployment_rate_by_sex_rural_urban_area_and_mari_2025, title = {Unemployment rate by sex, rural / urban area and marital status (%) | Africa (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_GEO_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-une-deap-sex-geo-mts-rt-unemployment-rate-by-sex-rural-urban-area-and-mari}} } ``` ## License Released under [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/). Original data © International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging. ## About Electric Sheep Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use `load_dataset()` to start working in seconds. Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica) --- _Provenance: ingested 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_GEO_MTS_RT_This dataset contains unemployment rate data for 45 African countries from 1994 to 2025, specifically the indicator Unemployment rate by sex, rural/urban area and marital status (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered for African countries, with 16,738 observations. It covers one core indicator (UNE_DEAP_SEX_GEO_MTS_RT) and includes details such as country codes, indicator codes, sex disaggregation, geographic and marital status classifications, year, observed values, and data quality flags. The data is harmonized by ILO and suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集源于国际劳工组织统计数据库（ILOSTAT）的公开失业统计，由Electric Sheep Africa团队进行系统化整理与重新封装。构建过程以非洲45个国家为地理单元，时间跨度自1994年至2025年，汇集了按性别、城乡区域及婚姻状况分组的失业率指标，最终形成包含16738条观测值的表格型数据资源。数据以Parquet格式存储，并附带标准化元数据说明，涵盖来源注释、加载指南及分析导向的背景信息，旨在为非洲劳动力市场研究提供可复用的结构化证据。

特点

数据集聚焦非洲区域失业率的多维分布特征，其核心特点在于同时纳入性别、城乡归属与婚姻状况三类社会人口维度，从而支持对劳动力市场边缘化群体的细粒度刻画。覆盖范围横跨45个非洲国家，时间序列纵贯三十余年，为跨国比较与趋势分析提供基础。数据以表格与文本双模态呈现，规模适中，且附带明确的使用指引与数据质量说明，便于研究者快速把握变量定义与缺失模式，避免因标签歧义导致误读。

使用方法

使用者可通过Hugging Face数据集库调用API加载数据，例如使用datasets.load_dataset函数获取数据集对象，并借助to_pandas方法将表格分支转换为数据框以支持后续分析。在建模之前，建议先行检查模式结构与缺失值分布，结合显式国家列与时间字段进行地理与时间维度的剖析。若需与其他非洲数据集整合，可利用国家、年份及指标字段进行连接。所有分析应核实原始来源中的变量定义与单位，并在成果中同时引用ILOSTAT与Electric Sheep Africa仓库。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于全球劳动力市场统计监测，其ILOSTAT数据库是劳动力统计领域的权威数据源。在此背景下，Electric Sheep Africa于2026年发布了该数据集，旨在将非洲地区按性别、城乡区域及婚姻状况分类的失业率数据进行系统化整理与开放共享。该数据集覆盖45个非洲国家、1994至2025年的16,738条观测记录，为研究非洲劳动力市场中的性别差异、城乡分化及婚姻状态对就业的影响提供了珍贵的结构化证据。其核心研究问题在于揭示非洲失业率的多维分布特征，并推动劳动经济学与区域发展研究的实证分析，对非洲政策制定和学术研究具有重要参考价值。

当前挑战

该数据集所应对的领域问题在于非洲劳动力市场统计长期面临数据碎片化、口径不一及覆盖不均等困境，尤其缺乏按性别、城乡和婚姻状况交叉分类的标准化失业率数据。构建过程中，主要挑战包括：跨国数据源在调查方法、时间频率与定义标准上的异质性，使跨年与跨地区比较难以直接进行；部分国家或年份的观测缺失导致面板数据不平衡，可能影响回归分析的稳健性；城乡与婚姻状况等分组变量的编码一致性难以完全保证，增加了分类建模的复杂度；此外，自报失业状态可能引入测量偏差，而快速变化的非正规经济部门进一步削弱了传统失业率指标的适用性。

常用场景

经典使用场景

在劳动经济学与区域发展研究的交汇处，该数据集凭借对非洲45国1994至2025年间失业率的性别、城乡与婚姻状况三维交叉刻画，成为剖析劳动力市场结构性分化的经典素材。研究者常以其为基座，开展按性别分层的失业率时序比较，或考察城乡二元结构下婚姻状态对劳动参与的影响差异，亦可用于验证奥肯定律等宏观理论在非洲语境下的适用性，进而揭示不同人口子群在就业机会获取上的非对称格局。

实际应用

在政策制定与国际发展实践中，该数据集为非洲各国劳工部门、国际组织及非政府机构提供了精细化的监测工具。决策者可据此定位失业率高企的特定人群，如农村已婚女性或城市单身青年，设计靶向就业扶持与技能培训方案；世界银行、国际劳工组织等机构亦可依托其开展国别比较与趋势预警，将性别与城乡维度纳入就业政策的成效评估框架，从而提升干预措施的精准度与公平性。

衍生相关工作

以该数据集为起点，研究者衍生出多类经典工作：其一，与非洲性别平等、教育程度及非正规就业数据集的跨源联结，催生了关于人力资本与失业持续期的因果推断研究；其二，基于其时空粒度构建的机器学习预测模型，被用于失业率短期波动预警；其三，围绕城乡婚姻状态差异的分解分析，推动了家庭经济学中劳动供给决策理论的区域化拓展，并成为Electric Sheep Africa系列数据论文中被引频次较高的案例之一。